1. Field:
This invention relates to self-optimizing machines, and more particularly relates to machines which self-optimize dynamically and in real time.
2. Prior Art:
Automatic machines are very common. Numerically controlled machining, drafting, and parts assembling have already made their great impacts. Automatically controlled electrical drills and other tools, automobile speed, room or house heating or cooling, home appliances, industrial processing equipment, and many other machines or equipment are becoming increasingly popular.
All these automatic machines use the rapidly developing microelectronics, particularly microprocessors. Unfortunately, these machines are not yet really controlled to optimize, dynamically and in real time, a desired performance characteristic such as fuel economy, product yield, purity, or cost, total cost or time of operation, and the like. The reasons are not difficult to see. In the case of automobile speed control, for example, to optimize a desired performance characteristic such as fuel economy or total cost of trip, a certain prediction or estimation equation is first developed and then programmed onto the microprocessor for execution. But the automobile speed depends not only on such variables as engine speed, temperature, and various settings on, e.g., the carburetor or distributor; automobile design and style; type and amount of gas and oil; load on the automobile; . . . , but also on many other usually neglected yet critical factors such as wind type, direction, or velocity; rain or snow; driver habit and condition; road age, slope, or condition; age and condition of automobile and engine; and the like, which change continuously.
The effects of these and many other variables and factors have never been completely determined, or even understood. These effects may also change from one month, week, day, or even instant to another. In addition, these factors or variables may interact strongly, i.e., have large synergistic or compensating effects. Many of these variables may not even be known or suspected.
Thus, there are definitely a large number of variables that may or may not be present, important, or critical. This large number may be 7, 31, 63, 127, 200, or even 1000 or 40,000. All must first be investigated to find out their functional relationship to the desired performance characteristic, so that this characteristic may be meaningfully optimized. Merely missing one or a few of the critical variables may make the optimization inefficient or even useless. Yet the very many variables and their surprisingly many interactions, and the vastly more tests to be performed normally would make the task of their understanding and optimizing hopelessly impossible to most people. The number n of tests to test m variables at only two levels or conditions each would require n=2.sup.m tests. For m=7, 31, 63, 127, and 200, n=128, 2.148.times.10.sup.9, 9.223.times.10.sup.18, 1.701.times.10.sup.38, and 1.606.times.10.sup.60, respectively.
Even for small m, e.g., 3 or 4, the usual practice is to take a few "samples" or "representative machines", on which to run limited number of tests within very narrow experimental ranges, with an experimental design and procedure which leaves much to be desired, i.e., without use of modern statistical techniques. Still, the investigator is often at a loss as to which samples to select. How many? How many tests on each sample? How to test? . . . The hope is that these tests would represent the entire population of machines (often a totally unjustified extrapolation). The microprocessor is then programmed according to these sampling results. No wonder these results can lead to erroreous conclusions. Often, the "optimized" conditions may not be optimal at all, but are far away from the optimum.
To compound the dilemma, the fact is that no two cars are identically the same. This is partly because of the unavoidable variable tolerances on the car components and the many interactions of these components. For example, two similar components may behave very differently if both are at the upper limits of their respective specifications, compared to when both are at the lower limits. The chance combination of which cars having what critical component combinations is totally unknow and unpredictable. The only way to truly and meaningfully optimize the performance characteristic of a particular automobile, then, is to determine the unique functional relationship of the many variables on the performance characteristic of this very, particular automobile itself, and then to set the levels or conditions of these many variables at the unique combination of their respective optimal values, at the very instant the functional relationship is determined and before this relationship changes.
In addition, because the car is in a dynamic environment, these tests, determinations, and variable settings must also be done dynamically, i.e., very rapidly, to be periodically checked and/or adjusted every hour, minute, or even fractional second as is needed. Such requirements can also only be achieved with systematic statistical designs and with the most modern microelectronics.
In the U.S. patent prior art, Hardaway's extreme parameter search control system (U.S. Pat. No. 3,466,430) adjusts one parameter or variable at a time. Russo (U.S. Pat. No. 3,576,976) provides a control system having a pattern recognition network in combination with a linear regression network. Smith's (U.S. Pat. No. 3,694,636) programmed digital computer process control also employs least squares regression fitting of collected data. Barron (U.S. Pat. No. 3,460,096 and 3,519,998) filed in 1966-1967 for his control system. But in these years, the microprocessor technology was not developed. Thus, none of these patented inventions deal with the unique problems addressed in this application, i.e., self-optimizing machines operative in real time with nanosecond speeds together with modern statistical designs (the prior art are totally silent on this) for the optimizing of large number of variables (7, 63, 511, 40,000, . . . ) within minutes or fractional seconds. There simply was no microprocessor in 1966-67 to fit into a car or patient's body, or a drill that was power-thrifty and can make billions of decisions within minutes or seconds, even if the efficient statistical designs to be described were used. But these patents do provide the background for use of control systems, actuators, calculators, timing circuits, D/A converters, storage memory, sensors, comparator, logic devices, sign detector, . . . , which are often useful in the practice of this invention.
Accordingly, an object of the present invention is to provide improved self-optimizing machines;
A further object of the invention is to provide self-optimizing machines equipped with modern microprocessors with nanosecond computing speeds and programmed to generate modern design matrices capable of handling tens, hundreds, thousands, or more variables in real time;
Another object of this invention is to provide self-optimizing machines which can be optimized dynamically and almost continuously and instantly;
A broad object of the invention is to provide self-optimizing machines based on controlled tests performed only on the very particular machines themselves without relying on extrapolations based on sampling test results obtained on other similar but often different or even irrelevant machines;
Another object of the invention is to optimize machines in real time by the installation thereon batteries of modern microelectronics, sensors, actuators, and related devices;
A further object of the invention is to provide small (less than 0.1 m.sup.3), rapid (nanosecond), efficient self-optimizing machines to fit into small or subcompact but fast moving cars or handdrills and rapidly reacting furnaces or dying patients for instantly correcting deviations from ideal conditions or dispensing necessary chemicals or drugs in optimum combinations in a continuous manner;